The present invention relates generally to processes for implementing Biometric Quality Control (QC), and more particularly to systems and methods for guiding and managing laboratory analytical process control operations.
Advances in laboratory technology have improved test performance beyond the ability of conventional process control systems to monitor performance effectively. The process control system used by most laboratories since the early 1980s is that of Westgard. (See, J. O. Westgard, P. L. Barry, M. R. Hunt: A Multi-Rule Shewhart Chart for Quality Control in Clinical Chemistry CLIN. CHEM. 27/3, 493-501, 1981). This system is based on a set of six core statistical rules, each having statistical power to detect random and systematic deviations from the norm.
In an effort to keep up with technology, Westgard evolved his core system with selection grids. These were quickly followed by power function graphs, Ops Specs charts, and QC Validator. Validator is software designed to recommend statistical rules based on analytical goals defined by the lab for each test. The latest version of Validator incorporates biological variation. Even with these improvements, the Westgard scheme continues to utilize a relatively complex statistical process control framework. This approach, often misapplied, results in frequent alarms normally judged false or unwarranted when compared to medical decision limits (medical relevance). There is no universally accepted alternative to Westgard. Labs must continue to use this system or to design and validate their own QC scheme in order to meet federal and accreditation requirements creating a patchwork of analytical process control schemes. While this patchwork generally results in acceptable outputs, it is marred by widely varying costs, inconsistent application and failed expectations. (See, Cooper William G., Quality control practices and preferences in today's clinical laboratory: A report for government regulators, decision makers and advisors, MLO, June 1997, pp. 57-65; Tetrault Gregory A., Steindel, Steven J., Daily Quality Control Exception Practices Data Analysis and Critique, CAP Q-Probe, 1994; and Howanitz Peter J., Tetrault Gregory A., Steindel Stephen J, Clinical Laboratory Quality Control: A Costly Process Now Out of Control). Labs have expressed both concern and frustration in journal articles, at public forums, in focus groups, to commercial sales representatives and through public commentary.
Laboratories around the world employ various schemes to control the analytical process. In the United States, the most common application is Westgard. Outside the US, applications range from prescriptive German RiliBAK rules to individualized applications and Westgard. European laboratories are generally more sophisticated in their approach employing biological variation and seeking standardization among laboratories.
Statistical rules for monitoring the analytical process, such as Westgard, can be used alone or in combination. If the rules are combined (multi-rule), then the power of error detection increases. Many labs may not understand how to apply the rules. Consequently, false error detection may frequently lead to test operator indifference. For example, a CAP Q-Probe study conducted in 1994 found that many laboratories respond to a QC error flag by merely repeating the control. No reasoned troubleshooting occurs unless the test operator is unsuccessful in getting the control value to fall within acceptable limits. Reasons for not immediately troubleshooting may include: easier to retest than troubleshoot, laziness, lack of knowledge, habit, and no accountability to troubleshoot correctly.
Rather than accept that some type of error might be present in the test system when a statistical flag occurs, labs may move immediately to some form of remedy rather than troubleshooting. The basic premise is that the statistical control system they use creates too many unwarranted errors so they automatically assume the error flag is false. The quickest remedy in this environment is to get the control value within range. To do so, some labs may repeat the control in hopes that the next value will be within limits (playing the odds), repeat with fresh control product, check or repeat calibration, or make up fresh reagent. Sometimes limited troubleshooting may be employed, including, for example, testing of assayed control materials to detect systematic error, looking at a history of control outliers, and calling the manufacturer for guidance or word of any national performance trends. Each of these actions is taken without any reasonable justification other than one of them usually corrects the error at least temporarily. Typically, the most common causes of QC error flags include random error, environmental conditions, control range too tight or incorrectly calculated, reagent (lot change, deterioration, contamination), control problems, calibration, sampling error, instrument malfunction, and poor maintenance.
Laboratory staff typically consider troubleshooting to be complex and often unguided. The production atmosphere of a typical lab and limited resources may contribute to a philosophy of avoiding troubleshooting unless absolutely necessary. The assumption follows that if troubleshooting could be focused, guided, or deemed necessary and productive, laboratory staff would engage in the effort. In general, it is desirable to make troubleshooting far easier by, for example, providing a QC system that identifies actionable error (i.e., eliminates false error detection), providing online troubleshooting advice, providing interactive online user groups so labs can exchange information readily, basing analytical process control on medical relevance limits (where appropriate), providing an analysis of the most frequently observed errors and determining the most likely cause of the error flag, providing instrument-specific troubleshooting guides, posting control stability claims and interlabs online, providing method group statistics, providing continuing education, and providing parallel lots for troubleshooting.
Another practice characteristic that is relevant to development of a Biometric model is when and at what frequency quality control materials are tested. Typically, controls are predominately tested at the beginning of each batch of patient specimens, e.g., in coagulation, hematology, immunoassay, and urinalysis, with possibly a little higher frequency of random placement in toxicology and special chemistry. General chemistry is one department where random placement of QC materials may often occur.